Additive Manufacturing (AM), a cornerstone of Industry 4.0, is expected to revolutionise production in practically all industries. However, multiple production challenges still exist, preventing its diffusion. In recent years, Machine Learning algorithms have been employed to overcome these hurdles. Nonetheless, the usage of these algorithms is constrained by the scarcity of data together with the challenges associated with accessing and integrating the information generated during the AM pipeline. In this work, we present a vendor-agnostic platform for AM that enables collecting, storing, analysing and linking the heterogeneous data of the complete AM process. We conducted an extensive analysis of the different AM datatypes and identified the most suitable technologies for storing them. Furthermore, we performed an in-depth study of the requirements of different AM stakeholders to develop a rich and intuitive Graphical User Interface. We showcased the specific usage of the platform for Powder Bed Fusion, one of the most popular AM processes, in a real industrial scenario, integrating specific existing modules for in-situ monitoring and real-time defect detection.

The quality of products has become a key factor for success in the current manufacturing industry. This is especially true in the aviation field where components for aircraft engines called honeycombs are produced. Due to their small dimension and peculiar shape, such components typically undergo a severe and cumbersome visual inspection by specialized operators. However, this process is highly prone to human error, requires a lot of time and a high number of undetected defects have been reported. In order to reduce the whole inspection time, ensure higher quality and guarantee standardization and process control, this paper presents an innovative strategy for the fully-automated inspection of honeycomb engine parts. The proposed solution is a two-phase process fully controlled by a robot, leveraging a camera as well as a purposely designed optic fibers sensor, coupled with Artificial Intelligence (AI) algorithms for the detection of different types of defects. To assess the functionality and validity of the proposed solution, a fully functioning prototype is described and characterized.

In the context of Industry 4.0, metal Additive Manufacturing (AM) is considered a promising technology for medical, aerospace and automotive fields. However, the lack of assurance of the quality of the printed parts can be an obstacle for a larger diffusion in industry. To this date, AM is most of the times a trial-and-error process, where the faulty artefacts are detected only after the end of part production. This impacts on the processing time and overall costs of the process. A possible solution to this problem is the in-situ monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build. In this paper, we describe a system for in-situ defects monitoring and detection for metal Powder Bed Fusion (PBF), that leverages an off-axis camera mounted on top of the machine. A set of fully automated algorithms based on Computer Vision and Machine Learning allow the timely detection of a number of powder bed defects and the monitoring of the object's profile for the entire duration of the build.